RadYOLO: Computationally Efficient 3D Object Detection and Segmentation in CT and MRI
Learn how RadYOLO enables efficient 3D object detection and segmentation in medical images like CT and MRI scans, and how it compares to other models like nnU-Net and nnDetection.
- Implement RadYOLO for 3D object detection in CT scans using PyTorch or TensorFlow
- Compare the performance of RadYOLO with nnU-Net and nnDetection on MRI images
- Apply data augmentation techniques to improve RadYOLO's detection accuracy on low-contrast medical images
- Evaluate the computational efficiency of RadYOLO on resource-constrained hardware
- Visualize and analyze the segmentation results of RadYOLO on 3D medical images
This research benefits radiologists, medical imaging analysts, and AI engineers working on computer vision tasks, particularly those dealing with 3D medical image analysis.
💡 RadYOLO achieves high detection performance while remaining computationally efficient, making it suitable for resource-constrained hardware.
🚀 RadYOLO: Efficient 3D object detection & segmentation in medical images! 💡
Key Takeaways
Learn how RadYOLO enables efficient 3D object detection and segmentation in medical images like CT and MRI scans, and how it compares to other models like nnU-Net and nnDetection.
Full Article
Abstract:
arXiv:2608.00508v1 Announce Type: cross Abstract: Object detection and segmentation in three-dimensional medical images is a very active area of research. However, most proposed deep learning models carry a high computational cost, and only few aim to be broadly applicable, achieve high detection performance, and remain fast to execute on resource-constrained hardware. To address this gap, we present RadYOLO, a 3D extension of YOLO11 tailored to medical images. We compare it with nnU-Net and nnD
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